the use of wsr-88d radar data at ncep
DESCRIPTION
The use of WSR-88D radar data at NCEP. Shun Liu SAIC/ National Centers of Environmental Prediction, Camp Springs, Maryland. Outline. WSR-88D radar data processing at NCEP Radar data quality control (QC) Radar reflectivity mosaic Radial wind assimilation. - PowerPoint PPT PresentationTRANSCRIPT
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The use of WSR-88D radar data at NCEP
Shun Liu
SAIC/ National Centers of Environmental Prediction, Camp Springs, Maryland
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Outline
• WSR-88D radar data processing at NCEP
• Radar data quality control (QC)
• Radar reflectivity mosaic
• Radial wind assimilation
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WSR88D-Radar Data Processing at NCEP
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WSR88D-Radar Data Processing at NCEP
• The images from radar observations are successfully used to detect severe weather and warn of thunderstorms.
• The use of high-resolution radar data to improve numerical weather prediction (NWP) is also active in the academic and research communities.
• However, progress in the use of high-resolution Level-II data in operational NWP models has been slower
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WSR88D-Radar Data Processing at NCEP
Problems of using radar data in operation:
• (1) the relatively large volume of radar data restricting the data to be transmitted to the operational center in real time
• (2) the radar data decoding software and storage taking excessive computational resources
• (3) the quality control (QC) problems of radar data further limiting the applications of radar data for operational use
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WSR88D-Radar Data Processing at NCEP
Figure from http://www.rap.ucar.edu/weather/radar/
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WSR88D-Radar Data Processing at NCEP
Radar data received at NCEP:
(1). VAD wind
(2). WSR88D Level-III (NIDS) data
(3). WSR88D Level 2.5 data
(4). WSR88D Level-II data
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Flowchart of radar data processing at NCEP
WSR88D-Radar Data Processing at NCEP
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Radar data quality control
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Radar data QC at NCEP
• To meet the high standard required by data assimilation, it is necessary to develop simple and efficient QC technique for operational applications.
• Radar data quality control is a necessary and initial step for operational applications of radar data.
• Develop statistically reliable QC techniques for automated detection of QC problems in operational environments
• Among various of radar data quality problems, radar measured velocities can be very different (≥10 m/s) from the air velocities in the presence of migrating birds.
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Radar data QC at NCEP
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Radar data QC at NCEP
Mean reflectivity (MRF)
refNnrefMRF /)(
max/ NNVDC vr
bmvrpsc JjIjIVSC /])(/)([
Velocity data coverage (VDC)
Along-beam perturbation velocity sign changes (VSC)
Along-beam velocity sign changes(SC)Standard deviation of radial wind (STD)
QC Parameters:
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Recorded QC parameters
0 400 800 1200 1600 2000 2400
0
2
4
6
8
10
time
MR
F (
dB
Z)
20
30
40
50
60
70
VD
C (
%)
27
30
33
36
39
42
VS
C (
%)
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Mean reflectivity
-2 0 2 4 6 8 10 12 14 160
100
200
300
400
500
Fre
quen
ce
MRF
-2 0 2 4 6 8 10 12 14 160
100
200
300
400
500
Fre
quen
ce
MRF
KTLX (From April 15 to June 15 2003) night Observation
No bird bird
Histogram of MRF for (a) non-contaminated and (b) bird-contaminated sweeps.
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10 20 30 40 50 60 700
100
200
300
400
500
600
Fre
quen
ce
VDC
10 20 30 40 50 60 700
100
200
300
400
500
600
Fre
quen
ceVDC
KTLX (From April 15 to June 15 2003) night Observation
Velocity Data Coverage
No bird bird
Histogram of VDC for (a) non-contaminated and (b) bird-contaminated sweeps.
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15 18 21 24 27 30 33 36 39 42 450
200
400
600
800
1000
1200
Fre
quen
ceVSC
15 18 21 24 27 30 33 36 39 42 450
100
200
300
400
500
600
Fre
quen
ce
VSC
KTLX (From April 15 to June 15 2003) night Observation
No bird bird
Along-beam Perturbation Velocity Sign Changes
Histogram of VSC (%) for (a) non-contaminated and (b) bird-contaminated sweeps.
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VDC
Probability Density Function
MRF
VSC
Prior probability density functions of MRF (a), VDC (b) and VSC (c).
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Bayes Identification
P(H0 |xi) = p(xi |H0)P(H0)/p(xi),
P(H1 |xi) = p(xi |H1)P(H1)/p(xi).
Denote the weather event by H0.Denote the bird event by H1.
Denote the i-th QC parameter by Xi.
p(xi)= p(xi |H0)P(H0)+p(xi |H1)P(H1)
P(H0)=N0/(N0+N1)P(H1)=N1/(N0+N1)
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VDC
Posterior Probability
MRF
VSC
Posterior probability distributions of MRF (a), VDC (b) and VSC (c)
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0 5 10 15 20 25 300
2000
4000
6000
8000
10000
12000
14000
16000
SN(%)
SN
KFWS 200909110605
KBUF 2009090513
23%
6%
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Performance of radar data QC
Observation (m/s)
anal
ysis
(m
/s)
anal
ysis
(m
/s)
Observation (m/s)
before QC after QC
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With QC
Goes image
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Zoom-in area
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Radar Reflectivity Mosaic
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1. raw radar data decoding
2. Radial velocity QC
3. Reflectivity QC
4. Single Radar Cartesian(coordinate transformation)
5. Output ref in Cartesian grid
6. Reflectivity 3D mosaic
Fig. 1
Fig. 2
Fig. 3
1. Flowchart of Radar data
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Fig. 1. Raw reflectivity from KATX radar at 0.5 deg elevation at radar coordinate and zoom-in area
2. Reflectivity in radar coordinate
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Figure is from NSSL
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Fig. 2. Reflectivity after SRC at Cartesian grid at 1500 m and zoom in area
The difference between Fig. 1 and Fig. 2 are because the reflectivity data are at different height after interpolation.
3. Reflectivity after SRC at 1500m
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Domain setup
1 2 3 4
5 6 7 8
Figure is from NSSL
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Tile ID ctrlat(ºN)
ctrlon(ºW)
nx ny nz SW corner NE corner
1 47.5 120 2001 1501 31 40, -130 55, -110
2 47.5 100 2001 1501 31 40, -110 55, -90
3 47.5 85 1001 1501 31 40, -90 55, -80
4 47.5 70 2001 1501 31 40, -80 55, -60
5 30 120 2001 2001 31 20, -130 40, -110
6 30 100 2001 2001 31 20, -110 40, -90
7 30 85 1001 2001 31 20, -90 40, -80
8 30 70 2001 2001 31 20, -80 40, -60
Domain Specifications
Table is from NSSL
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Test in domain 6
Reflectivity mosaic fields at 500 and 2500 m
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Radar wind assimilation in GSI
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• 3D variational (3DVAR) algorithm (Wu et al. 2002).
• The cost function is defined by
1 11/ 2[ ( ) ( )]TJ H H x B x x y R x y
Radar wind assimilation in GSI
nsiwsincosvcoscosu),(V r
• Forward model
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GSI namelist for radial wind assimilation
• WSR-88D radar wind observations
VAD, level-2.5 data, level-II data.
&OBS_INPUTdfile(06)='radarbufr', dtype(06)='rw', dplat(06)=' ', dsis(06)='rw', dval(06)=1.0, dthin(06)=0,
&SUPEROB_RADARdel_azimuth=5.,del_elev=.25,del_range=5000.,del_time=.5,elev_angle_max=5.,minnum=50,range_max=100000., l2superob_only=.false., /
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Level-II radar wind bufr
Radial wind bufr:• SSTN | 001018 | RADAR STATION IDENTIFIER (SHORT) |• | RPID | 001198 | RADAR STATION IDENTIFIER |• | VOID | 001212 | RADAR VOLUME ID (IN THE FORM DDHHMM) |• | SCID | 001213 | RADAR SCAN ID (RANGE 1-21) |• | ANAZ | 002134 | ANTENNA AZIMUTH ANGLE |• | ANEL | 002135 | ANTENNA ELEVATION ANGLE |• | YEAR | 004001 | YEAR |• | MNTH | 004002 | MONTH |• | DAYS | 004003 | DAY |• | HOUR | 004004 | HOUR |• | MINU | 004005 | MINUTE |• | SECO | 004006 | SECOND |• | STDM | 004075 | TIME DISPLACEMENT IN MINUTES FROM BASE TIME (SHORT) |• | CLAT | 005002 | LATITUDE (COARSE ACCURACY) |• | SUPLAT | 005197 | LATITUDE OF RADIAL WIND SUPEROB(COARSE ACCURACY) |• | CLON | 006002 | LONGITUDE (COARSE ACCURACY) |• | DIST | 006021 | DISTANCE (FROM ANTENNA TO GATE CENTER) |• | SUPLON | 006197 | LONGITUDE OF RADIAL WIND SUPEROB(COARSE ACCURACY) |• | DIST125M | 006210 | DISTANCE (FROM ANTENNA TO GATE CENTER) IN UNITS OF 125M |• | SELV | 007001 | HEIGHT OF STATION |• | HEIT | 007007 | HEIGHT OF RADIAL WIND SUPEROB |• | HSMSL | 007030 | HEIGHT OF STATION GROUND ABOVE MSL |• | HSALG | 007032 | HEIGHT OF ANTENNA ABOVE GROUND |• | HREF | 021001 | HORIZONTAL REFLECTIVITY |• | DMVR | 021014 | DOPPLER MEAN RADIAL VELOCITY |• | DVSW | 021017 | DOPPLER VELOCITY SPECTRAL WIDTH |• | HNQV | 021019 | HIGH NYQUIST VELOCITY |• | RWND | 021194 | RADIAL WIND RADIAL WIND SUPEROB |• | RWAZ | 021195 | AZIMUTH OF RADIAL WIND SUPEROB |• | RSTD | 021196 | STANDARD DEVIATION OF RADIAL WIND SUPEROB |• | VOCP | 021197 | VOLUME COVERAGE PATTERN |• | QCRF | 033250 | QUALITY MARK FOR REFLECTIVITIES ALONG RADIAL LINE |• | QCRW | 033251 | QUALITY MARK FOR WINDS ALONG RADIAL LINE |• | MGPT | 050004 | BUFR MESSAGE NUMBER |
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Radar data flow in GSI
Outer loop:
a. read-in radar data
b. Compute O-B
c. Call Inner loop
1). Compute cost function and gradient
2). Update analysis increment
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30 h